基于两种干旱指数对淮河流域干旱程度变化的分析
Analysis on the Change of Drought Degree in Huaihe River Basin Based on Two Drought Indexes
摘要: 通过淮河1960~2012年间154个气象站的降水资料，选择标准化降水蒸散指数(SPEI)与标准化降水指数(SPI)作为干旱指标，对淮河流域的干旱特征变化进行分析。结果显示：(1) 时间尺度相同的情况下，干旱指数SPEI相对干旱指数SPI对干旱的评估偏低，整体评估偏湿。(2) 两个指数的时间序列图均表明淮河流域的干湿转变在逐渐平缓，且有干旱趋势，但SPEI表现更为平稳，波动较小。(3) 淮河流域整体属于洪旱灾害频发区，流域南部、东部、东北部是洪涝灾害频发区；流域中部、南部为干旱灾害频发区。(4) 淮河流域西部和北部面临较为严峻的干旱风险。对于Z值，SPEI普遍比SPI高，这表明在干旱趋势上，SPEI指数比SPI指数更倾向干旱趋势。
Abstract: Based on the precipitation data of 154 meteorological stations in Huaihe River from 1960 to 2012, the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) are selected as drought indicators to analyze the changes of Drought Characteristics in Huaihe River Basin. The results show that: (1) Under the same time scale, the evaluation of drought-by-drought index SPEI is lower than that by drought index SPI, and the overall evaluation is wet. (2) The time series diagrams of the two indexes show that the dry wet transition in the Huaihe River Basin is gradually gentle, and there is a drought trend, but SPEI is more stable and has less fluctuation. (3) The Huaihe River Basin as a whole belongs to the area with frequent flood and drought disasters, and the south, East and northeast of the basin are the areas with frequent flood and waterlogging disasters; The central and southern parts of the basin are drought disaster prone areas. (4) The West and north of Huaihe River Basin are facing severe drought risk. For Z value, SPEI is generally higher than SPI, which indicates that SPEI index is more inclined to drought trend than SPI index.
文章引用：吴昊俣, 龙凌云. 基于两种干旱指数对淮河流域干旱程度变化的分析[J]. 气候变化研究快报, 2024, 13(6): 1564-1582. https://doi.org/10.12677/ccrl.2024.136168

参考文献

[1] 方国华, 涂玉虹, 闻昕, 等. 1961-2015年淮河流域气象干旱发展过程和演变特征研究[J]. 水利学报, 2019, 50(5): 598-611.
[2] 郑远长. 全球自然灾害概述[J]. 中国减灾, 2000, 10(1): 14-19.
[3] 李忆平, 李耀辉. 气象干旱指数在中国的适应性研究进展[J]. 干旱气象, 2017, 35(5): 709-723.
[4] Keyantash, J.A. and Dracup, J.A. (2004) An Aggregate Drought Index: Assessing Drought Severity Based on Fluctuations in the Hydrologic Cycle and Surface Water Storage. Water Resources Research, 40, W09304. [Google Scholar] [CrossRef] 
[5] 罗漫雅, 耿广坡, 周洪奎, 等. 常用气象干旱指数SPEI和 SPI在陕北地区的适用性研究[J]. 湖北农业科学, 2020, 59(21): 42-50.
[6] 容锦盟, 周丹, 罗静, 等. 4种干旱指标在华北地区气象干旱监测中的适用性分析[J]. 干旱地区农业研究, 2019, 37(1): 259-267.
[7] 陈丽, 徐长春, 孙琪, 等. 典型干旱指数在干旱内陆河流域的适用性分析[J]. 江西农业学报, 2021, 33(8): 71-77.
[8] 郑金丽, 严子奇, 周祖昊, 等. 基于综合干旱指数的鄱阳湖流域干旱时空分异特征研究[J]. 水利水电技术, 2021, 52(8): 91-100.
[9] 张俊, 陈桂亚, 杨文发. 国内外干旱研究进展综述[J]. 人民长江, 2011, 42(10): 65-69.
[10] 张强, 姚玉璧, 李耀辉, 等. 中国西北地区干旱气象灾害监测预警与减灾技术研究进展及其展望[J]. 地球科学进展, 2015, 30(2): 196-213.
[11] Fu, R., Chen, R., Wang, C., Chen, X., Gu, H., Wang, C., et al. (2022) Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning. Remote Sensing, 14, Article 1662. [Google Scholar] [CrossRef] 
[12] Shrivastava, S., Kiran, R.U., Bal, P.K. and Singh, K.K. (2022) Estimation of the Standardized Precipitation Evapotranspiration Index (SPEI) Using a Multilayer Perceptron Artificial Neural Network Model for Central India. Pure and Applied Geophysics, 179, 1461-1473. [Google Scholar] [CrossRef] 
[13] Yao, N., Li, Y., Liu, Q., Zhang, S., Chen, X., Ji, Y., et al. (2022) Response of Wheat and Maize Growth-Yields to Meteorological and Agricultural Droughts Based on Standardized Precipitation Evapotranspiration Indexes and Soil Moisture Deficit Indexes. Agricultural Water Management, 266, Article ID: 107566. [Google Scholar] [CrossRef] 
[14] Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I. (2010) A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23, 1696-1718. [Google Scholar] [CrossRef] 
[15] 朱昊宇, 姬钰. 2011-2020年淮河流域水资源变化特征研究[J]. 水利技术监督, 2022(3): 162-165, 173.
[16] 姜健俊, 万瑞容, 牛战富. 淮河流域主要社会经济指标统计分析研究[J]. 治淮, 2022(1): 34-36.
[17] 林慧, 王景才, 黄金柏, 蒋陈娟. 基于SPI和SPEI的淮河中上游流域气象干旱时空分布特征对比研究[J]. 水资源与水工程学报, 2019, 30(6): 59-67.
[18] 林慧. 基于CMIP6模式的淮河中上游流域气象干旱特征及演变趋势研究[D]: [硕士学位论文]. 扬州: 扬州大学, 2021.
[19] 黎敏, 吕海深, 欧阳芬. 基于Delta方法的淮河流域气候变化预测分析[J]. 人民长江, 2012, 43(7): 11-14, 46.
[20] 杨赤, 刘志雨, 李洋. 淮河流域水文极值预测模型研究[J]. 气候变化研究进展, 2015, 11(1): 22-30.
[21] 董晓华, 张庆玉, 张特, 喻丹, 刘冀, 魏冲. 三种长期定量降水产品在淮河流域干旱监测中的潜力[J]. 农业工程学报, 2021, 37(11): 93-103.
[22] Huang, Z.Y. and Hu, J.J. (2022) Study of the Spatial and Temporal Patterns of the Water Footprint of Grain Production in the Huaihe River Basin. Scientific Journal of Intelligent Systems Research, 4.
[23] Fang, G., Li, X., Xu, M., Wen, X. and Huang, X. (2021) Spatiotemporal Variability of Drought and Its Multi-Scale Linkages with Climate Indices in the Huaihe River Basin, Central China and East China. Atmosphere, 12, Article 1446. [Google Scholar] [CrossRef] 

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